Dataset simplification of n-dimensional signals captured for asset tracking

ABSTRACT

Methods, systems, and devices for dataset simplification of N-dimensional signals captured for asset tracking are provided. An example method involves obtaining raw data from a data source onboard an asset and determining whether obtainment of the raw data results in satisfaction of a data logging trigger. The method further involves, when the data logging trigger is satisfied, performing a dataset simplification algorithm on a target set of data within the raw data to generate a simplified set of data, wherein the target set of data contains a time-variant N-dimensional signal, N&gt;=1, and the dataset simplification algorithm is generalized for all N&gt;=1. The method further involves transmitting the simplified set of data to a server.

CROSS-REFERENCE

This application claims the benefit under 35 U.S.C. § 120 as acontinuation of U.S. patent application Ser. No. 17/211,671, titled“Dataset Simplification of N-Dimensional Signals captured for AssetTracking,” filed Mar. 24, 2021, which claims the benefit under 35 U.S.C.§ 120 as a continuation of U.S. patent application Ser. No. 16/928,071,titled “Dataset Simplification of N-Dimensional Signals Captured forAsset Tracking,” filed Jul. 14, 2020, which claims the benefit under 35U.S.C § 1.119(e) to U.S. Provisional Application Ser. No. 63/039,480,titled “Dataset Simplification of Multidimensional Signals Captured byAsset Tracking Devices,” filed Jun. 16, 2020, each of which is hereinincorporated by reference in its entirety.

FIELD

The present disclosure relates to telematics, and in particular to thecollection of data for asset tracking for telematics systems.

BACKGROUND

A telematics system may track the location of an asset, such as avehicle, and other data related to the asset, directly through the assetor through an asset tracking device located onboard the asset. Theasset, or its asset tracking device, may communicate with a satellitenavigation system, such as a Global Positioning System (GPS), GlobalNavigation Satellite System (GNSS), cellular tower network, Wi-Finetwork, or other system to track the location of the asset. An assettracking device may collect additional information via sensors on theasset tracking device, such as accelerometer data or other data. Anasset tracking device may also collect information through a dataconnection with the asset itself, such as, in the case of a vehicularasset, through an onboard diagnostic port from which engine speed,battery temperature, fuel level, tire pressure, outside temperature, orother asset data may be obtained. Such data may be received and recordedby technical infrastructure of the telematics system and used in theprovision of telematics services, such as fleet management tools, or forfurther data analysis.

SUMMARY

According to an aspect of the disclosure, a method for capturing rawdata that contains a multidimensional signal at an asset or assettracking device for transmission to a telematics system is provided. Themethod involves obtaining raw data from a data source onboard an assetand determining whether obtainment of the raw data results insatisfaction of a data logging trigger. When the data logging trigger issatisfied, a dataset simplification algorithm is performed on a targetset of data within the raw data to generate a simplified set of data,wherein the target set of data contains a time-variant multidimensionalsignal and the dataset simplification algorithm is generalized for anymultidimensional signal. The method further involves transmitting thesimplified set of data to a server.

Performing the dataset simplification algorithm may involve applying adimensionally generalized Ramer-Douglas-Peucker algorithm to the targetset of data to select points in the target set of data for inclusion inthe simplified set of data that have minimum distances to a referenceline running through the target set of data that exceed a thresholdvalue, wherein each minimum distance is achieved by a directcalculation. Determination of a minimum distance for a sample point inthe target set of data to the reference line may be achieved bydetermining a reference point on the reference line that is minimallydistant from that sample point by evaluating t_(min) in the followingequation:

$t_{\min} = \frac{t_{p} + {\sum_{n = 1}^{N}{\left( {y_{p_{n}} - y_{0_{n}}} \right)*a_{n}}}}{1 + {\sum_{n = 1}^{N}a_{n}^{2}}}$

wherein t_(p) represents the time value (t) at the sample point (p), Nrepresents the total number of non-time dimensions in themultidimensional signal, n represents the n^(th) non-time dimension inthe multidimensional signal, y_(p) _(n) represents the value (y) of thesample point (p) in the n^(th) dimension (n), y₀ _(n) represents thevalue (y) of the first point (0) in the n^(th) dimension (n), the firstpoint (0) being first with respect to time, a_(n) represents the slope(a) of the reference line in the n^(th) dimension (n) with respect totime, and t_(min) represents the time (t) at the reference point, thepoint at which the reference line is minimally distant (min) from thesample point, and determining the distance from that sample point to thereference point by the following equation:

${❘{R\left( {L,p} \right)}❘} = \sqrt{\left( {t_{\min} - t_{p}} \right)^{2} + {\sum\limits_{n = 1}^{N}\left( {y_{0_{n}} + {a_{n}*t_{\min}} - y_{p_{n}}} \right)^{2}}}$

wherein, t_(min), t_(p), N, n, y₀ _(n) , a_(n), and y_(p) _(n) aredefined as in the previous equation, and |R(L, p)| represents thedistance (R) from the sample point (p) to the reference point on thereference line (L).

Performing the dataset simplification algorithm may involve: (i)defining a reference line through the target set of data from a firstpoint in the set to a last point in the set with respect to time, (ii)determining a minimum distance to the reference line for all points inthe target set of data between the first point and the last point,wherein determination of the minimum distance is a dimensionallygeneralized and direct calculation, (iii) selecting the point that hasthe largest minimum distance to the reference line, (iv) if the minimumdistance from the selected point to the reference line is larger than athreshold value, logging the selected point for inclusion in thesimplified set of data, and (v) repeating steps (i) through (iv) onsubdivided portions of the set, each of which is bounded by the firstpoint in the set, a to-be-saved point, or the last point in the set, asthe case may be, using, for each subdivided portion, a line definedbetween the first point and the last point of that subdivided portion asa respective reference line for that subdivided portion, until there areno points in any subdivided portion that are minimally distant from therespective reference line of that subdivided portion by at least thethreshold value.

The multidimensional signal may contain at least two data types that arerecorded in different units, and the method further may include scalingthe units of at least one dimension of the multidimensional signal priorto application of the dataset simplification algorithm. The two datatypes that are recorded in different units may be obtained fromdifferent data sources.

The multidimensional signal may include a first dimension forlatitudinal position of the asset and a second dimension forlongitudinal position of the asset. The multidimensional signal mayfurther include a third dimension for altitudinal position of the asset.The multidimensional signal may include a first dimension for anaccelerometer signal in an X direction, a second dimension for anaccelerometer signal in a Y direction, and a third dimension for anaccelerometer signal in a Z direction. The raw data may be obtained byan asset tracking device onboard the asset, and the data source mayinclude one or more of: an onboard diagnostic port of the asset and asensor of the asset tracking device.

According to another aspect of the disclosure, an asset tracking devicethat captures raw data that contains a multidimensional signal fortransmission to a telematics system is provided. The asset trackingdevice includes an interface layer to obtain raw data from a data sourceonboard an asset, wherein the raw data contains a target set of datathat contains a time-variant multidimensional signal. The asset trackingdevice further includes a memory to store the raw data, and a controllerto determine whether obtainment of the raw data results in satisfactionof a data logging trigger, and when the data logging trigger issatisfied, perform a dataset simplification algorithm on the target setof data to generate a simplified set of data, wherein the datasetsimplification algorithm is generalized for any multidimensional signal.The asset tracking device further includes a communication interface totransmit the simplified set of data to a server.

The multidimensional signal may contain at least two data types that arerecorded in different units, and the units of at least one dimension ofthe multidimensional signal are scaled prior to application of thedataset simplification algorithm. The two data types that are recordedin different units may be obtained from different data sources. A firstdata type of the two data types may be a positional data type and asecond data type of the two data types may be a speed data type. Theasset may be a vehicle, the positional data type may be obtained from alocating device onboard the asset tracking device, and the speed datatype may be obtained through an onboard diagnostic port of the vehicle.

According to yet another aspect of the disclosure, a system forcapturing data from an asset tracking device is provided. Systemincludes an asset tracking device onboard an asset, the asset trackingdevice configured to obtain raw data from a data source onboard theasset, wherein the raw data contains a target set of data that containsa time-variant multidimensional signal, determine whether obtainment ofthe raw data results in satisfaction of a data logging trigger, and whenthe data logging trigger is satisfied, perform a dataset simplificationalgorithm on the target set of data to generate a simplified set ofdata, wherein the dataset simplification algorithm is generalized forany multidimensional signal, and transmit the simplified set of data.The system further includes one or more servers to receive thesimplified set of data from the asset tracking device, and record thesimplified set of data in an asset tracking database.

The multidimensional signal may contain at least two data types that arerecorded in different units, and the units of at least one of thedimensions of the multidimensional signal may be scaled prior toapplication of the dataset simplification algorithm. The two data typesthat are recorded in different units may be obtained from different datasources. A first data type of the two data types may be a positionaldata type and a second data type of the two data types may be a speeddata type. The asset may be a vehicle, the positional data type may beobtained from a locating device onboard the asset tracking device, andthe speed data type may be obtained through an onboard diagnostic portof the vehicle.

According to yet another aspect of the disclosure, another method forcapturing raw data that contains a multidimensional signal at an assetor asset tracking device for transmission to a telematics system isprovided. The method involves obtaining raw data from a data sourceonboard an asset, determining whether obtainment of the raw data resultsin satisfaction of a data logging trigger, and, when the data loggingtrigger is satisfied, performing a dataset simplification algorithm on atarget set of data within the raw data to generate a simplified set ofdata, wherein the target set of data contains a time-variantN-dimensional signal, N>=1, and the dataset simplification algorithm isgeneralized for all N>=1, and transmitting the simplified set of data toa server.

Performing the dataset simplification algorithm may involve applying adimensionally generalized Ramer-Douglas-Peucker algorithm to the targetset of data to select points in the target set of data for inclusion inthe simplified set of data that have minimum distances to a referenceline running through the target set of data that exceed a thresholdvalue, wherein determination of each minimum distance is direct for allN>=1. Determination of each minimum distance for a sample point in thetarget set of data to the reference line may be achieved by determininga reference point on the reference line that is minimally distant fromthat sample point by evaluating t_(min) in the following equation:

$t_{\min} = \frac{t_{p} + {\sum_{n = 1}^{N}{\left( {y_{p_{n}} - y_{0_{n}}} \right)*a_{n}}}}{1 + {\sum_{n = 1}^{N}a_{n}^{2}}}$

wherein, t_(p) represents the time value (t) at the sample point (p), Nrepresents the total number of non-time dimensions in the N-dimensionalsignal, n represents the n^(th) dimension in the N-dimensional signal,y_(p) _(n) represents the value (y) of the sample point (p) in then^(th) dimension (n), y₀ _(n) represents the value (y) of the firstpoint (0) in the n^(th) dimension (n), the first point (0) being firstwith respect to time, a_(n) represents the slope (a) of the referenceline in the n^(th) dimension (n) with respect to time, and t_(min)represents the time (t) at the reference point, the point at which thereference line is minimally distant (min) from the sample point, anddetermining the distance from that sample point to the reference pointby the following equation:

${❘{R\left( {L,p} \right)}❘} = \sqrt{\left( {t_{\min} - t_{p}} \right)^{2} + {\sum\limits_{n = 1}^{N}\left( {y_{0_{n}} + {a_{n}*t_{\min}} - y_{p_{n}}} \right)^{2}}}$

wherein, t_(min), t_(p), N, n, y₀ _(n) , a_(n), and y_(p) _(n) aredefined as in the previous equation, and |R(L,p)| represents thedistance (R) from the sample point (p) to the reference point on thereference line (L).

Performing the dataset simplification algorithm may involve: (i)defining a reference line through the target set of data from a firstpoint in the set to a last point in the set with respect to time, (ii)determining a minimum distance to the reference line for all points inthe target set of data between the first point and the last point,wherein determination of the minimum distance is a dimensionallygeneralized and direct calculation, (iii) selecting the point that hasthe largest minimum distance to the reference line, (iv) if the minimumdistance from the selected point to the reference line is larger than athreshold value, logging the selected point for inclusion in thesimplified set of data, and (v) repeating steps (i) through (iv) onsubdivided portions of the set, each of which is bounded by the firstpoint in the set, a to-be-saved point, or the last point in the set, asthe case may be, using, for each subdivided portion, a line definedbetween the first point and the last point of that subdivided portion asa respective reference line for that subdivided portion, until there areno points in any subdivided portion that are minimally distant from therespective reference line of that subdivided portion by at least thethreshold value.

The N-dimensional signal may contain at least two data types that arerecorded in different units, and the method may further involve scalingthe units of at least one dimension of the multidimensional signal priorto application of the dataset simplification algorithm. The two datatypes that are recorded in different units may be obtained fromdifferent data sources. The N-dimensional signal may include a firstdimension for latitudinal position of the asset and a second dimensionfor longitudinal position of the asset. The N-dimensional signal mayfurther include a third dimension for altitudinal position of the asset.The N-dimensional signal may include a first dimension for anaccelerometer signal in an X direction, a second dimension for anaccelerometer signal in a Y direction, and a third dimension for anaccelerometer signal in a Z direction. The raw data may be obtained byan asset tracking device onboard the asset, and the data source mayinclude one or more of: an onboard diagnostic port of the asset and asensor of the asset tracking device.

According to yet another aspect of the disclosure, another assettracking device that captures raw data that contains a multidimensionalsignal for transmission to a telematics system is provided. The assettracking device includes an interface layer to obtain raw data from adata source onboard an asset, wherein the raw data contains a target setof data that contains a time-variant N-dimensional signal, N>=1. Theasset tracking device further includes a memory to store the raw data,and a controller to determine whether obtainment of the raw data resultsin satisfaction of a data logging trigger, and, when the data loggingtrigger is satisfied, perform a dataset simplification algorithm on thetarget set of data to generate a simplified set of data, wherein thedataset simplification algorithm is generalized for all N>=1, and acommunication interface to transmit the simplified set of data to aserver.

The N-dimensional signal may contain at least two data types that arerecorded in different units, and the units of at least one of thedimensions of the multidimensional signal may be scaled prior toapplication of the dataset simplification algorithm. The two data typesthat are recorded in different units may be obtained from different datasources. A first data type of the two data types may be a positionaldata type and a second data type of the two data types may be a speeddata type. The asset may be a vehicle, the positional data type may beobtained from a locating device onboard the asset tracking device, andthe speed data type may be obtained through an onboard diagnostic portof the vehicle.

According to yet another aspect of the disclosure, another system forcapturing data from an asset tracking device is provided. The systemincludes an asset tracking device onboard an asset, the asset trackingdevice configured to obtain raw data from a data source onboard theasset, wherein the raw data contains a target set of data that containsa time-variant N-dimensional signal, N>=1, determine whether obtainmentof the raw data results in satisfaction of a data logging trigger, and,when the data logging trigger is satisfied, perform a datasetsimplification algorithm on the target set of data to generate asimplified set of data, wherein the dataset simplification algorithm isgeneralized for all N>=1, and transmit the simplified set of data. Thesystem further includes one or more servers to receive the simplifiedset of data from the asset tracking device, and record the simplifiedset of data in an asset tracking database.

The N-dimensional signal may contain at least two data types that arerecorded in different units, and the units of at least one dimension ofthe multidimensional signal may be scaled prior to application of thedataset simplification algorithm. The two data types that are recordedin different units may be obtained from different data sources. A firstdata type of the two data types may be a positional data type and asecond data type of the two data types may be a speed data type. theasset may be a vehicle, the positional data type may be obtained from alocating device onboard the asset tracking device, and the speed datatype may be obtained through an onboard diagnostic port of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an example system for capturing datafrom an asset or asset tracking device. The asset or asset trackingdevice captures raw data that contains a multidimensional signal andsimplifies the multidimensional signal for transmission to a telematicssystem.

FIG. 2 is a block diagram of an example asset tracking device thatcaptures raw data that contains a multidimensional signal fortransmission to a telematics system.

FIG. 3 is a flowchart of an example method for capturing raw data thatcontains a multidimensional signal at an asset or asset tracking devicefor transmission to a telematics system.

FIG. 4 is a flowchart of an example method for performing a datasetsimplification algorithm on a set of data the contains amultidimensional signal.

FIGS. 5A, 5B, 5C, 6A, 6B, and 6C are data plots that illustrate theapplication of a multidimensional dataset simplification algorithm toexample raw data captured by an asset or asset tracking device.

DETAILED DESCRIPTION

A large telematics system may collect data from a very high number ofassets, either directly or through asset tracking devices. A very highnumber of assets may be capable of collecting such large amounts of datathat the technical infrastructure of a telematics system could beoverwhelmed if all of the data were to be transmitted to, and processedby, the telematics system. Therefore, assets and asset tracking devicesgenerally transmit only a small proportion of the total number of datapoints collected to their telematics system, and discard the remainder.

An asset or asset tracking device may determine which data points totransmit to its telematics system, and which data points to discard,directly on the device. The determination of which points to transmit,and which points to discard, may be made in a number of ways. In onesimplistic example, one may employ simple periodic sampling, wherebyonly the data that is spaced apart by regularly spaced time intervals isretained for transmission, and all remaining data is discarded.Alternatively, in an improved example, one may algorithmically determinewhich points of data are the most significant for the purposes of atelematics service, and retain only these most significant data pointsfor transmission. Applying a dataset simplification algorithm thatretains only the most significant data points may ensure that thetechnical infrastructure of its telematics system is not overwhelmedwith unnecessary amounts of data that is not useful in the provision ofa telematics service.

Although the application of a dataset simplification algorithm may leadto data collection efficiencies for the telematics system that receivesthe data, the application of such algorithms may be computationallyintensive for the asset or asset tracking device that collects the data,particularly in the case where the asset or asset tracking devicecollects many different types of data and performs a datasetsimplification algorithm on each of these types of data independently.

The present disclosure provides techniques for applying a datasetsimplification algorithm on raw data that is in the form of anN-dimensional signal, and proposes that applying a dimensionallygeneralized dataset simplification algorithm on an N-dimensional signalmay be more computationally efficient as compared to the application ofseveral instances of a unidimensional dataset simplification algorithmon the same data in the form of several separate sets of unidimensionalsignals. The term “signal” in this context refers to the signal of datatypes that are collected over time. That is, for example, it may be morecomputationally efficient to apply a dimensionally generalized datasetsimplification algorithm on a three-dimensional accelerometer signalthat comprises an accelerometer signal in the X direction, andaccelerometer signal in the Y direction, and an accelerometer signal inthe Z direction, over time, than it is to apply a unidimensional datasetsimplification algorithm on the accelerometer signal in the X directionover time, the accelerometer signal in the Y direction over time, andagain on the accelerometer signal in the Z direction over time. Ingeneral, such a multidimensional signal may be any bundle of signalsthat would otherwise be simplified independently.

Thus, an asset or asset tracking device may collect data of a pluralityof different types, and perform dimensionally generalized datasetsimplification algorithms on multidimensional signals within the data tosimplify the data in a computationally efficient manner, prior totransmission to its telematics system. Further, by performing datasetsimplification algorithms on bundles of signals that would haveotherwise been simplified independently, the asset or asset trackingdevice may make more accurate determinations as to which points are themost significant points, which may result in more reliable datacollection for its telematics system.

FIG. 1 is a schematic diagram of an example system 100 for capturingdata from an asset or asset tracking device. In the present example, thesystem 100 includes an asset tracking device 110 located at an asset102.

In some examples, the asset tracking device 110 may be a self-containeddevice installed at the asset 102. In other examples, the asset trackingdevice 110 may be a tracking device that is integrated into the asset102, in which case the data captured by the asset tracking device 110may be referred to as being captured by the asset 102 itself. In eithercase, the data captured may be referred to as being captured by an assettracking device.

The asset tracking device 110 collects data, such as the location of theasset tracking device 110 or sensor data from sensors onboard the assettracking device 110. In some examples, such as in the case where theasset 102 is a vehicle and the asset tracking device 110 is a separatedevice, the asset tracking device 110 may collect data directly from theasset 102 through an onboard diagnostic port. This data is indicatedgenerally as raw data 104.

For collecting location data, the system 100 may include a locatingsystem (not shown) for tracking the locations of one or more assettracking devices, including the asset tracking device 110, such as aGlobal Positioning System (GPS), a Global Navigation Satellite System(GNSS), a cellular tower network, Wi-Fi networks, or another systemwhich enables the monitoring of the location of asset tracking devices.

For exemplary purposes, the asset 102 is shown as a vehicular asset: atransport truck. However, the asset 102 may include any type ofvehicular asset, such as a passenger vehicle, construction vehicle,other utility vehicle, naval vessel, airplane, or any other vehicularasset that may be tracked by an asset tracking device. The asset 102 mayalso include any non-vehicular asset, such as a transport trailer,shipping container, pallet, shipped good, or any other non-vehicularasset which may be tracked by an asset tracking device. Further, inother examples, the asset 102 may include a vehicle or other asset thatincludes an integrated tracking device to capture data related to theasset 102.

The system 100 further includes a telematics system 120 that includes anasset tracking database that may record location data, trip/travelhistories, accelerometer data, temperature sensor data, vehicle speeddata, and other data captured by assets and/or asset tracking devices,including the asset tracking device 110. The telematics system 120 mayfurther store user accounts and other data associated with the assettracking devices for the provision of telematics services.

The technical infrastructure of the telematics system 120 includes oneor more servers or computing devices, indicated, for example, as aserver 122. The server 122 includes a communication interface tocommunicate with asset tracking devices via one or more computingnetworks and/or telecommunication networks, a memory to store data, anda controller to execute the methods performed by the telematics system120 as described herein. For example, the server 122 is shown toprovision a telematics services module 124 which provides telematicsservices, such as asset tracking and reporting services, to clientdevices (not shown) using data collected from asset tracking devices.

A portion of the raw data 104 collected by the asset tracking device 110is transmitted to the telematics system 120, indicated as transmitteddata 112. The remainder of the raw data 104 that is not transmitted isdiscarded. The raw data 104 includes a multidimensional signal 106,which is simplified by the asset tracking device 110 into a simplifiedmultidimensional signal 114 for inclusion in the transmitted data 112 bythe application of a dimensionally generalized dataset simplificationalgorithm.

The multidimensional signal 106 is time-variant, and is a signal thatincludes multiple streams of raw data collected over time. For example,the multidimensional signal 106 may include a first dimension for anaccelerometer signal in the X direction, a second dimension for anaccelerometer signal in the Y direction, and a third dimension for anaccelerometer signal in the Z direction, each of which are collectedover time by the asset tracking device 110. As another example, themultidimensional signal 106 may include a first dimension forlatitudinal position of the asset 102 and a second dimension forlongitudinal position of the asset 102, and in some examples, a thirddimension for an altitudinal position of the asset 102, each of whichare collected over time by the asset tracking device 110. In otherwords, the multidimensional signal is a time-variant N-dimensionalsignal, with N>1, and the dataset simplification algorithm isgeneralized for all N>1.

The data types that are included in the multidimensional signal 106 maybe of related data types that are recorded in the same units (such as inthe case of a multidimensional accelerometer data signal, or in the caseof a multidimensional positional data signal). However, in otherexamples, the multidimensional signal 106 may include a multitude ofrelated and/or unrelated data types (e.g., a combination ofaccelerometer data, speed, location, engine data, etc.).

As will be seen below, any of these multidimensional signals may beprocessed through a dataset simplification algorithm that is generalizedfor any dimensionality (for all N>1) to improve the computationalefficiency of the simplification of such data. As will be seen below,performing the dataset simplification algorithm on the multidimensionalsignal 106 rather than on several unidimensional signals independentlymay improve processing performance of the asset tracking device 110, andmay further improve the reliability of the collection of data by thetelematics system 120. Example methods by which the asset trackingdevice 110 obtains and simplifies such data are discussed in greaterdetail below.

Further, in some examples, the dataset simplification algorithm mayfurther be generalized for the case where the signal to be simplified iseither unidimensional (N=1) or multidimensional (N>1), and the samegeneralized dataset simplification algorithm may be applied to a signalof any dimensionality (N>=1) thereby enabling the use of a common modelfor dataset simplification for all dimensions, which may improve thedesign, implementation, and maintenance of such systems.

FIG. 2 is a block diagram of an example asset tracking device 200 thatcollects and simplifies raw data that contains a multidimensional signalfor transmission to a telematics system. The asset tracking device 200may be understood to be one example of the asset tracking device 110 ofFIG. 1 .

The asset tracking device 200 is onboard an asset 202, which may besimilar to the asset 102 of FIG. 1 . As described above, the asset 202may be a vehicular or non-vehicular asset.

The asset tracking device 200 includes an interface layer 210 to obtainraw data 204 from a data source onboard the asset 202. The data sourcemay include a sensor of the asset tracking device 200, a sensor of theasset 202, a locating device of the asset tracking device 200 (e.g., aGPS device located on the asset tracking device 200), a locating deviceof the asset 202 (e.g., a GPS device located on the asset 202), or, inexamples in which the asset 202 is a vehicle and the asset trackingdevice 200 is located onboard the asset 202, an onboard diagnostic portof the asset 202. In any case, the interface layer 210 obtains raw data204 from such a data source and includes one or more interfaces forreceiving raw data 204 from the data source.

The asset tracking device 200 further includes a memory 220 that storesat least a portion of the raw data 204 collected through the interfacelayer 210. The memory 220 may include read-only memory (ROM),random-access memory (RAM), flash memory, magnetic storage, opticalstorage, and similar, or any combination thereof. The memory 220 mayinclude a raw data buffer to store the raw data 204, and a loggingmemory to store the data that is to-be-saved and transmitted totechnical infrastructure of a telematics system, shown here as a server201, which may be similar to the server 122 of FIG. 1 .

The raw data 204 includes a subset that is to be simplified as amultidimensional signal, denoted herein as a target set of data 205. Thetarget set of data 205 contains a time-variant multidimensional signal206, similar to the multidimensional signal 106 as discussed above withrespect to FIG. 1 . The multidimensional signal 206 may includepositional data, accelerometer data, or any other data that is to besimplified by a dataset simplification algorithm.

Although only a single target set of data 205 and multidimensionalsignal 206 are shown, this is for illustrative purposes only, and it isto be understood that the memory 220 may contain several target sets ofdata 205 and multidimensional signals 206, to be processed as discussedherein. Further, the amount of raw data 204 stored on the memory 220 atany given time may depend on a number of factors, such as the memorysize of the memory 220, the frequency with which such data is to belogged, and the bandwidth available for such data to be transmitted to atelematics system.

The asset tracking device 200 further includes a controller 230 toexecute data logging instructions 232 and dimensionally generalizeddataset simplification instructions 234. The controller includes one ormore of a processor, microprocessor, microcontroller (MCU), centralprocessing unit (CPU), processing core, state machine, logic gate array,application-specific integrated circuit (ASIC), field-programmable gatearray (FPGA), or similar, capable of executing, whether by software,hardware, firmware, or a combination of such, the actions performed bythe controller 230 as described herein. The controller 230 includes amemory, which may include ROM, RAM, flash memory, magnetic storage,optical storage, and similar, or any combination thereof, for storinginstructions and data as discussed herein, including the data logginginstructions 232 and dimensionally generalized dataset simplificationinstructions 234.

The data logging instructions 232 cause the controller 230 to determinewhether obtainment of the raw data 204 results in satisfaction of a datalogging trigger.

In some examples, determining whether obtainment of the raw data 204results in satisfaction of a data logging trigger may involve monitoringthe raw data 204 for a particular feature in the raw data 204. Forexample, a data logging trigger may be satisfied when the asset trackingdevice 200 determines from the raw data 204 that the asset 202 haseither stopped or started moving. As another example, the asset trackingdevice 200 may continually monitor the trends in the data of any of itsdata types (e.g., position), and a data logging trigger may be satisfiedwhen the asset tracking device 200 determines that, for any given datatype, the actual collected data differs from the trend by a thresholdamount. Thus, for example, when the asset 202 veers off course from itsexpected path of travel, a data logging trigger may be satisfied.

In other examples, determining whether obtainment of the raw data 204results in satisfaction of a data logging trigger may involve monitoringan operating condition of the asset tracking device 200 for when aparticular operating condition is met. For example, a data loggingtrigger may be satisfied when a hardware limitation of the assettracking device 200 is met, such as, for example, when a raw data bufferreaches a limit to the amount of raw data it can store, when a timerexpires, or based on telematics service standards, such as, for example,a frequency with which the data at the telematics system is to beupdated.

When a data logging trigger is satisfied, the data logging instructions232 cause the dimensionally generalized dataset simplificationinstructions 234 to be executed, and a dataset simplification algorithmis performed on a target set of data 205 to generate a simplified set ofdata 222. The target set of data 205 may or may not relate to the typeof data monitored by the data logging trigger that was satisfied. Thatis, for example, if a data logging trigger related to the position ofthe asset 202 was satisfied, the dimensionally generalized datasetsimplification instructions 234 may perform a dataset simplificationalgorithm on positional data. Alternatively, when a raw data bufferreaches its limit, the dimensionally generalized dataset simplificationinstructions 234 may be executed on all types of data stored therein.

The dimensionally generalized dataset simplification algorithmdetermines which data points in the multidimensional signal 206 shouldbe retained in the simplified multidimensional signal 224 and logged fortransmission to the server 201. That is, the dimensionally generalizeddataset simplification algorithm determines which data points in themultidimensional signal 206 are most useful to providing a telematicsservice (i.e., to be saved), and which data points provide little usefulinformation to a telematics service (i.e., to be discarded).

The dataset simplification algorithm may include a line simplificationalgorithm that reduces a curve of raw data composed of line segments(e.g., the multidimensional signal 206) into a similar curve with fewerpoints. An example of such a line simplification algorithm in itsunidimensional form is the Ramer-Douglas-Peucker algorithm, whichinvolves the application of a two-dimensional Pythagorean calculation todetermine the distance from a sample point to a reference line runningthrough the data. However, the dataset simplification algorithmperformed by the dimensionality generalized dataset simplificationinstructions 234 is generalized for any multidimensional signal 206. Inother words, the multidimensional signal 206 is a time-variantN-dimensional signal, with N>1, and the dataset simplification algorithmis generalized for all N>1.

One form of multidimensional dataset simplification algorithm that maybe applied by the dimensionally generalized dataset simplificationinstructions 234 may be understood to be a modifiedRamer-Douglas-Peucker algorithm that is modified to be dimensionallygeneralized. Such a modified Ramer-Douglas-Peucker algorithm may involvethe application of a dimensionally-generalized Pythagorean calculationto determine the distance from a sample point to a reference linerunning through the data. However, other multidimensional datasetsimplification algorithms are contemplated.

As mentioned, the dataset simplification algorithm is to be applied tothe target set of data 205 to select points in the target set of data205 for inclusion in the simplified set of data 222. In the example of amodified Ramer-Douglas-Peucker algorithm, the points for inclusion inthe simplified set of data 222 have minimum distances to a referenceline running through the target set of data 205 that exceed a thresholdvalue. When modified to be dimensionally generalized, determination ofthe minimum distances is achieved by a direct calculation, discussed ingreater detail in FIGS. 5A-6C, below, rather than by a series ofcalculations on a series of unidimensional calculations. Such amultidimensional algorithm may result in computational efficiencies andimproved data logging reliability.

Further, in some examples, the dataset simplification algorithm mayfurther be generalized for the case where the signal to be simplified iseither unidimensional (N=1) or multidimensional (N>1), and the samegeneralized dataset simplification algorithm may be applied to a signalof any dimensionality (N>=1) thereby enabling the use of a common modelfor dataset simplification for all dimensions, which may improve thedesign, implementation, and maintenance of such systems. In suchexamples, in the application of a modified Ramder-Douglas-Peuckeralgorithm, the determination of the minimum distances is direct for allN>=1.

The asset tracking device 200 further includes a communication interface240 to transmit the simplified set of data 222, which includes thesimplified multidimensional signal 224, to the server 201. Thecommunication interface 240 may include a cellular modem, such as anLTE-M modem, CAT-M modem, or other cellular modem configured forbidirectional communication via the network with which asset trackingdevice 200 may communicate with the server 201.

FIG. 3 is a flowchart of an example method 300 for collecting raw dataat an asset or asset tracking device that contains a multidimensionalsignal, and simplifying the multidimensional signal for transmission toa telematics system. The method 300 may be understood to be one exampleof how the asset tracking device 200 of FIG. 2 collects and simplifiesraw data containing a multidimensional signal. Thus, for exemplarypurposes, the method 300 will be described with reference to the assettracking device 200 of FIG. 2 . Further, the blocks of the method 300are elaborated upon above with reference to the appropriate componentsof the asset tracking device 200 of FIG. 2 . However, it is to beunderstood that the method 300 may be applied by other assets and/orasset tracking devices.

At block 302, the interface layer 210 of the asset tracking device 200obtains raw data 204 from a data source onboard the asset 202. Asdescribed above, the data source may include a sensor, locating device,or onboard diagnostic port of a vehicle.

At block 304, the controller 230 determines whether obtainment of theraw data 204 results in satisfaction of a data logging trigger. The datalogging trigger is included in the data logging instructions 232. Asdescribed above, the data logging trigger is a trigger that determineswhen the raw data 204 is to be evaluated for simplification and loggedfor transmission to a telematics system.

At block 306, when the data logging trigger is satisfied, the controller230 performs a dimensionally generalized dataset simplificationalgorithm on the target set of data 205 within the raw data 204 togenerate the simplified set of data 222. The target set of data 205contains the time-variant multidimensional signal 206, and thesimplified set of data 222 contains the simplified multidimensionalsignal 224. In other words, the multidimensional signal 206 is atime-variant N-dimensional signal, with N>1, and the datasetsimplification algorithm is generalized for all N>1. Further, in someexamples, the dataset simplification algorithm may further begeneralized for the case where the signal to be simplified is eitherunidimensional (N=1) or multidimensional (N>1), and the same generalizeddataset simplification algorithm may be applied to a signal of anydimensionality (N>=1). In either case, the dataset simplificationalgorithm is generalized for any multidimensional signal 206. Asdescribed above, the dataset simplification algorithm determines whichdata points should be recorded for transmission to the telematics systemand which data points should be discarded. The simplified set of data222 is logged for transmission.

At block 308, the communication interface 240 transmits the simplifiedset of data 222 to the server 201. The simplified set of data 222 may beused in a telematics system as appropriate.

The method 300 may be embodied in instructions (such as the data logginginstructions 232 and dimensionally generalized dataset simplificationinstructions 234) stored on a non-transitory machine-readable storagemedium that is executable by the controller 230 to perform the method300. The non-transitory machine-readable storage medium may include ROM,RAM, flash memory, magnetic storage, optical storage, and similar, orany combination thereof, for storing instructions and data as discussedherein.

Thus, a non-transitory machine-readable storage medium may containinstructions that when executed cause the controller 230 to obtain rawdata 204 from a data source onboard the asset 202 and determine whetherobtainment of the raw data 204 results in satisfaction of a data loggingtrigger. When the data logging trigger is satisfied, the instructionsmay cause the controller 230 to perform a dataset simplificationalgorithm on a target set of data 205 within the raw data 204. Thetarget set of data 205 contains a time-variant multidimensional signal206. The dataset simplification algorithm is to generate a simplifiedset of data 222 that contains a simplified multidimensional signal 224.The dataset simplification algorithm is generalized for anymultidimensional signal. The instructions may further cause thecontroller 230 to transmit the simplified set of data 222 to the server201.

FIG. 4 is a flowchart of an example method 400 for performing a datasetsimplification algorithm on a set of data that contains amultidimensional signal. The method 400 may be understood to be oneexample of how a dimensionally generalized dataset simplificationalgorithm may be performed on raw data collected by an asset trackingdevice. For example, the method 400 may be understood to be one exampleof how the block 306 of the method 300 of FIG. 3 may be performed by theasset tracking device 200 of FIG. 2 . Thus, for convenience, descriptionof the method 400 is made with reference to the asset tracking device200 of FIG. 2 . However, this is not limiting, and the method 400 may beperformed by other systems and/or devices.

The method 400 is illustrated as being applied on an example set of rawdata in FIGS. 5A, 5B, 5C, 6A, 6B, and 6C. The example set of raw datashown may be understood to be one example of the target set of data 205of FIG. 2 . For illustrative purposes, description of the method 400 isalso made with reference to this target set of data 205 in FIGS. 5A, 5B,5C, 6A, 6B, and 6C. However, this is not limiting, and the method 400may be performed on other sets of raw data.

At block 402, the controller 230 selects the target set of data 205 fromthe raw data 204. As described above, the target set of data 205 may beselected based on the data logging trigger that was satisfied. Thetarget set of data 205 may be any set of data that includes sometime-variant multidimensional signal 206 that is to be simplified priorto transmission to a telematics system. In some cases, the target set ofdata 205 may also be any subdivided portion of a higher level target setof data, as discussed below. With reference to FIG. 5A, the target setof data 205 includes a two-dimensional signal having dimensions (Y₁) and(Y₂) that vary with time (t). The target set of data 205 may be, forexample, an X dimension of accelerometer data (e.g., (Y₁)) and a Ydimension of accelerometer data (e.g., (Y₂)) collected over time. As canbe seen, the data points in the target set of data 205 vary with respectto (Y₁) and (Y₂) over time.

At block 404, a reference line is defined through the target set of data205, from the first point in the set to the last point in the set withrespect to time. With reference to FIG. 5A, the reference line (L-0) isdefined through points (a) and (b), and travels through thethree-dimensional space defined by (Y₁), (Y₂), and (t). The referenceline (L-0) is a straight line running from point (a) to point (b). Insome examples, the first and last points (a) and (b) may be marked asto-be-saved for inclusion in the simplified set of data 222.

In general, a reference line (L) is defined by the following equation:

L(t)=(y ₀ ₁ +a ₁ *t, . . . ,y ₀ _(n) +a _(n) *t, . . . ,y ₀ _(N) +a _(N)*t)  (1)

In the above equation (1), (t) represents time, (y₀₁) represents thevalue (y) of the first point (0) in the first dimension, (a₁) representsthe slope (a) of the reference line in the first dimension with respectto time, (y₀ _(n) ) represents the value (y) of the first point (0) inthe n^(th) dimension (n), the first point (0) being first with respectto time, (a_(n)) represents the slope (a) of the reference line in then^(th) dimension (n) with respect to time, and (N) represents the totalnumber of non-time dimensions in the multidimensional signal (i.e., twodimensions (Y₁) and (Y₂) in the target set of data 205).

At block 406, the controller 230 determines a minimum distance to thereference line for all points in the target set of data 205 between thefirst point and the last point. With reference to FIG. 5A, thecontroller 230 determines the distance from all points between points(a) and (b) to the reference line (L-0). The determination of a minimumdistance from a sample point (p-0) to the reference line (L-0) isdimensionally generalized, and in this case, it is a three-dimensionaldistance calculated through the three-dimensional space defined by (Y₁),(Y₂), and (t).

In general, the determination of a minimum distance from any samplepoint (p) to a reference line (L) is also direct, in that it is achievedin a single step (i.e., by the application of a single mathematicalequation). This direct multidimensional determination is in contrast tousing a dataset simplification algorithm that acts on a unidimensionalsignal, such as the Ramer-Douglas-Peucker algorithm, to simplify amultidimensional signal.

In general, a reference point on a reference line (L) that is minimallydistant from a sample point (p) to the reference line (L) may bedetermined, in a dimensionally generalized manner, by evaluating(t_(min)) in the following equation:

$\begin{matrix}{t_{\min} = \frac{t_{p} + {\sum_{n = 1}^{N}{\left( {y_{p_{n}} - y_{0_{n}}} \right)*a_{n}}}}{1 + {\sum_{n = 1}^{N}a_{n}^{2}}}} & (2)\end{matrix}$

In the above equation (2), (t_(p)) represents the time value (t) at thesample point (p), (N) represents the total number of non-time dimensionsin the multidimensional signal (i.e., two dimensions (Y₁) and (Y₂) inthe target set of data 205), (n) represents the n^(th) dimension in themultidimensional signal, (y_(p) _(n) ) represents the value (y) of thesample point (p) in the n^(th) dimension (n), (y₀ _(n) ) represents thevalue (y) of the first point (0) in the n^(th) dimension (n), the firstpoint (0) being first with respect to time, (a_(n)) represents the slope(a) of the reference line in the n^(th) dimension (n) with respect totime, and (t_(min)) represents the time (t) at the reference point, thepoint at which the reference line is minimally distant (min) from thesample point (p).

The above equation (2) is derived by differentiating the followingequation with respect to time, which is a generalized equation toprovide the distance from a sample point (p) to the reference line (L)at any level of dimensionality.

|R(L,p)|=√{square root over ((t−t _(p))²+Σ_(n=1) ^(N)(y ₀ _(n) =a _(n)*t−y _(p) _(n) )²)}  (3)

In the above equation (3), (t), (t_(p)), (N), (n), (y₀ _(n) ), (a_(n)),and (y_(p) _(n) ) are defined as in the previous equation (2), and |R(L,p)| represents the distance (R) from the sample point (p) to thereference point on the reference line (L).

Evaluating equation (3) at the time (t_(min)) determined from equation(1) provides the minimum distance from a sample point (p) to a referencepoint on the reference line (L), generalized for any multidimensionalsignal, as shown in the equation below:

|R(L,p)|=√{square root over ((t _(min) −t _(p))²+Σ_(n=1) ^(N)(y ₀ _(n)=a _(n) *t _(min) −y _(p) _(n) )²)}  (4)

Thus, the minimum distance from a sample point (p) to the reference line(L) is determined. This determination is made for all points in thetarget set of data 205. This determination is in contrast to using adataset simplification algorithm that acts on a unidimensional datasignal, such as the Ramer-Douglas-Peucker algorithm, to simplify amultidimensional signal.

The above equation (2) may be simplified for any (N), some examples ofwhich are provided in Table 1 below. Alongside each of the simplifiedequations there is also provided the number of mathematical operationsin that equation that cause high processor load (“HPL”). Mathematicaloperations that cause high processor load include multiplication,division, square roots, and squares, but exclude addition andsubtraction.

HPL OPERA- (N) Equation (2) simplified for example values of (N) TIONS 1$t_{\min} = \frac{t_{p} + {\left( {y_{p_{1}} - y_{0_{1}}} \right)*a_{1}}}{1 + a_{1}^{2}}$3 2$t_{\min} = \frac{t_{p} + {\left( {y_{p_{1}} - y_{0_{1}}} \right)*a_{1}} + {\left( {y_{p_{2}} - y_{0_{2}}} \right)*a_{2}}}{1 + a_{1}^{2} + a_{2}^{2}}$5 3$t_{\min} = \frac{t_{p} + {\left( {y_{p_{1}} - y_{0_{1}}} \right)*a_{1}} + {\left( {y_{p_{2}} - y_{0_{2}}} \right)*a_{2}} + {\left( {y_{p_{3}} - y_{0_{3}}} \right)*a_{3}}}{1 + a_{1}^{2} + a_{2}^{2} + a_{3}^{2}}$7

Further, the above equation (4) may be simplified for any (N), someexamples of which are provided in Table 1 below. Similarly, alongsideeach of the simplified equations there is also provided the number ofmathematical operations in that equation that cause high processor load.

HPL OPERA- (N) Equation (4) simplified for example values of (N) TIONS 1${❘{R\left( {L,p} \right)}❘} = \sqrt{\begin{matrix}{\left( {t_{\min} - t_{p}} \right)^{2} +} \\\left( {y_{0_{1}} + {a_{1}*t_{\min}} - y_{p_{1}}} \right)^{2}\end{matrix}}$ 4 2 ${❘{R\left( {L,p} \right)}❘} = \sqrt{\begin{matrix}{\left( {t_{\min} - t_{p}} \right)^{2} +} \\{\left( {y_{0_{1}} + {a_{1}*t_{\min}} - y_{p_{1}}} \right)^{2} +} \\\left( {y_{0_{2}} + {a_{2}*t_{\min}} - y_{p_{2}}} \right)^{2}\end{matrix}}$ 6 3 ${❘{R\left( {L,p} \right)}❘} = \sqrt{\begin{matrix}{\left( {t_{\min} - t_{p}} \right)^{2} +} \\{\left( {y_{0_{1}} + {a_{1}*t_{\min}} - y_{p_{1}}} \right)^{2} +} \\{\left( {y_{0_{2}} + {a_{2}*t_{\min}} - y_{p_{2}}} \right)^{2} +} \\\left( {y_{0_{3}} + {a_{3}*t_{\min}} - y_{p_{3}}} \right)^{2}\end{matrix}}$ 8

Any of the equations shown in Table 1 and Table 2, and furthersimilarly-derived equations for higher values of (N), may be included inthe dimensionally generalized dataset simplification instructions 234.The dimensionally generalized dataset simplification instructions 234may include instructions to analyze any given target set of data 205 todetermine the number of dimensions in the multidimensional signal 206therein, and to select an appropriate equation to use for themultidimensional signal 206 based on its number of dimensions.

As can be seen from Table 1 and Table 2, the number of HPL operationsincreases with N for equation (2) and equation (4). However, the numberof HPL operations to perform either equation for any N>1 is lower thanthe number of HPL operations that would be required to perform the sameequation N times when N=1. For example, five HPL operations areperformed by equation (2) when N=2, but six HPL operations are performedif equation (2) is performed twice when N=1. Thus, it is lesscomputationally intensive on a processor to perform equation (2) withN=2 (i.e., when simplifying a multidimensional signal of N=2) than it isto perform equation (2) twice when N=1 (i.e., when simplifying twoseparate unidimensional signals of each N=1). Similar computationalsavings are provided by equation (4) as well. Further, as will be seenbelow, the distance from each data point in the target set of data 205may be compared against a single threshold value (Z) to determinewhether or not the point is to be saved for inclusion in the simplifiedset of data 222, which is more computationally efficient than making aseparate comparison to a separate threshold value for several separatedata types. Thus, it is more computationally efficient to simplify amultidimensional signal than it is to simplify several unidimensionalsignals containing the same data, as would be the case when applying theRamer-Douglas-Peucker algorithm to several unidimensional signals.

At block 408, the controller 230 selects the point in the target set ofdata 205 that has the largest minimum distance to the reference line.With reference to FIG. 5A, this is the sample point (p-0) shown, whichis minimally distant from the reference line (L-0) by the distance(R-0).

At block 410, the controller 230 determines whether the largest minimumdistance is greater than a threshold. With reference to FIG. 5A, thedistance (R-0) is checked against the threshold distance (4. Thethreshold distance (Z) may be referred to as the “curve error”, as itdenotes the amount by which it is considered acceptable for thesimplified multidimensional signal 224 (i.e., the simplified curve) tovary from the raw data 204.

The threshold distance (Z) may be predetermined based on a desireddegree to which the target set of data 205 is to be simplified. A largervalue (Z) may result in greater degree simplification, and a lower value(Z) may result in a lesser degree of simplification.

In cases in which the target set of data 205 contains at least two datatypes that are recorded in different units, the determination of whetherthe threshold value (Z) is exceeded may be influenced by the units thateach data type is measured in. For example, where (Y₁) is Zaccelerometer data and (Y₂) is vehicle speed data, the value of (Y₂) canbe expected to vary more widely than the value of (Y₁). Even in somecases in which the target set of data 205 contains two signals that areof the same data type, but are measured in different units (e.g., onetemperature sensor measures temperature in degrees Celsius and anothertemperature sensor measures temperature in degrees Fahrenheit), onesignal may have more influence on whether the threshold value (Z) is metthan another. To correct for such skew in the data, the method 400 mayinvolve scaling the units of at least one dimension of themultidimensional signal 206 prior to determination of the minimumdistances so that the effect that each dimension has on whether thethreshold distance (Z) is met can be controlled. For example, where (Y₁)is Z accelerometer data and (Y₂) is vehicle speed data, the value of(Y₂) can be expected to vary more widely than the value of (Y₁), andthus the (Y₁) dimension may have a scaling factor (e.g., a factor of 5)applied to it, so that each data type may have a comparable amount ofimpact on whether the threshold value (Z) is met. A scaling factor mayalso be applied to the time dimension so that the effect that the timedimension (and the units it is recorded in) has on whether the thresholddistance (Z) is met can be controlled. Thus, the dataset simplificationalgorithm may involve the application of single threshold value (Z), asopposed to multiple threshold values (Z) for separate signals, themultidimensional signal 206 may be simplified in a computationallyefficient manner.

In cases in which all of the data types included in the multidimensionalsignal 206 are of the same data type and are measured in the same units(e.g., where (Y₁) is X accelerometer data measured in (g) and (Y₂) is Yaccelerometer data measured in (g)), the threshold value (Z) need not bescaled with respect to either dimension of the multidimensional signal206.

The selection of the threshold value (Z) may depend on a maximumacceptable error for any one, or combination of, signals in themultidimensional signal 206. For example, each signal may scaled down(or up, as the case may be) according to the maximum acceptable errorfor each other signal in the multidimensional signal 206. As a numericalexample, if the multidimensional signal 206 includes accelerometer datafor which there is a maximum allowable error of +/−10 m/s² (and theaccelerometer data is measured in m/s²), and the multidimensional signal206 further includes vehicle speed data for which there is a maximumallowable error of +/−1 km/h (and the vehicle speed data is measured inkm/h), then the signal for the accelerometer data may be scaled down bya ratio of 1/10 (i.e., the ratio of the maximum acceptable error of theother signal divided by the maximum acceptable error of the signal).

In general, if the point with the largest minimum distance is less thanthe threshold, then the method 400 is ended. If the point with thelargest minimum distance is greater than the threshold, the method 400proceeds to block 412.

At block 412, the controller 230 logs the selected point, which has thelargest minimum distance to the reference line, to be included in thesimplified set of data 222 for transmission to a telematics system. Withreference to FIG. 5A, it can be seen that the distance (R-0) from thesample point (p-0) to the reference line (L-0) is greater than thethreshold value (Z), and thus the sample point (p-0) is saved forinclusion in the simplified set of data 222.

In general, if the target set of data 205 can be further subdivided,blocks 402 to 412 may then be repeated on such subdivided portions. Atblock 414, the controller 230 determines whether the target set of data205 can be subdivided into smaller subdivided portions. If the targetset of data 205 cannot be divided into smaller subdivided portions, suchas, for example, if there are no further points in the target set ofdata 205 that can form a subdivided portion, or if there are no datapoints in the target set of data 205 that are more distant to thereference line than the threshold value, then the method 400 is ended.If the target set of data 205 can be subdivided, then the method 400proceeds to block 416.

At block 416, the target set of data 205 is subdivided into smallersubdivided portions. Each subdivided portion is bounded by either thefirst point in the higher level target set of data 205 (e.g., point (a),a to-be-saved point (e.g., the previously saved point (p-0), or the lastpoint in the higher level target set of data 205 (e.g., point (b)), asthe case may be. For each subdivided portion, the reference line to beused for evaluation of the minimum distances is a line defined betweenthe first point and the last point of that subdivided portion. Themethod 400 may be repeated this way for increasingly smaller subdividedportions of the target set if data 205 until there are no points in anysubdivided portion that are minimally distant from the respectivereference line of that subdivided portion by at least the thresholdvalue. Thus, the target set of data 205 is reduced to a simplified setof data 222. FIGS. 5B, 5C, 6A, 6B, and 6C further illustrate theabove-described iterative process.

In FIG. 5B, the original target set of data 205, through which theoriginal reference line (L-0) was defined, is divided into twosubdivided portions, 205-1 and 205-2, through each of which is defined anew respective reference line (L-1), (L-2), respectively. The originaltarget set of data 205 may be referred to as the higher level target setof data, and each of the subdivided portions 205-1 and 205-2 may bereferred to as subdivided portions thereof. In the target set of data205-1, the sample point (p-1) having the greatest minimum distance tothe reference line (L-1) is not more distant from the reference line(L-1) than the threshold value (2), and thus this sample point (p-1) isnot saved for inclusion in the simplified set of data 222. In the targetset of data 205-2, the sample point (p-2) having the greatest minimumdistance to the reference line (L-2) is more distant (R-2) from thereference line (L-2) than the threshold value (2), and thus this samplepoint (p-2) is saved for inclusion in the simplified set of data 222.Since there are no points in the target set of data 205-1 that are moredistant to the reference line (L-1) than the threshold value (2), thetarget set of data 205-1 cannot be further reduced into a furthersubdivided portion.

In FIG. 5C, the target set of data 205-2 is divided into two subdividedportions, 205-3 and 205-4, through each of which is defined a newrespective reference line (L-3), (L-4), respectively. In the target setof data 205-3, the sample point (p-3) having the greatest minimumdistance to the reference line (L-3) is not more distant from thereference line (L-3) than the threshold value (2), and thus this samplepoint (p-3) is not saved for inclusion in the simplified set of data222. In the target set of data 205-4, the sample point (p-4) having thegreatest minimum distance to the reference line (L-4) is more distant(R-4) from the reference line (L-4) than the threshold value (2), andthus this sample point (p-4) is saved for inclusion in the simplifiedset of data 222. Since there are no points in the target set of data205-3 that are more distant to the reference line (L-3) than thethreshold value (2), the target set of data 205-3 cannot be furtherreduced into a further subdivided portion.

Similarly, in FIG. 6A, the target set of data 205-4 is divided into twosubdivided portions, 205-5 and 205-6, through each of which is defined anew respective reference line (L-5), (L-6), respectively, and for whichthe sample points (p-5) and (p-6) that are minimally distant to therespective lines (L-5), (L-6), are more distant than the threshold (2),and are saved for inclusion in the simplified set of data 222. Further,in FIG. 6B, the target sets of data 205-6 and 205-6 are divided intofurther subdivided portions 205-7, 205-8, 205-9, and 205-10, from whichonly the sample point (p-7), which is minimally distant from itsreference line (L-7) by a distance greater than the threshold value (2),is saved to be included in the simplified set of data 222. In FIG. 6C,the completed simplified set of data 222 is shown, which includes points(p-0), (p-2), (p-4), (p-5), (p-6), (p-7), and which is not reducible toany further subdivided portions. The remaining points from the rawtarget set of data 205 that are not included in the simplified set ofdata 222 are discarded.

Thus, the asset tracking device 200 (or the asset itself) may collectraw data 204 that contains a target set of data 205 that includes amultidimensional signal 206 of a plurality of different data types, andperform a dimensionally generalized dataset simplification algorithm onthe multidimensional signal 206 to simplify the data in acomputationally efficient manner prior to transmission to its telematicssystem.

Further, by performing a dataset simplification algorithm on amultidimensional signal rather than on a series of unidimensionalsignals, the asset tracking device 200 may make more accuratedeterminations as to which points are the most significant points, whichmay result in more reliable data collection for its telematics system.

The data types that are included in the target set of data 205, and thusin the multidimensional signal 206, may be set out in the dimensionallygeneralized dataset simplification instructions 234. That is, thedimensionally generalized dataset simplification instructions 234 mayinclude instructions for the controller 230 to identify the individualthreads of data in the raw data 204 that are to be bundled together intoa target set of data 205, and may further include instructions to flagsuch data or compile such data so that it may be processed through adimensionally generalized dataset simplification algorithm.

A target set of data 205 may include data types that are obtained fromthe same data source. In many cases, such data types may each berecorded in the same units, which simplifies calculations in the datasetsimplification algorithm. For example, X, Y, and Z accelerometer signalsmay all be obtained from an accelerometer onboard the asset trackingdevice 200 and may all be recorded in standard gravity units (i.e., g)).

However, another target set of data 205 may include data types that arenot directly related, and which may be obtained from different datasources, but which may be indirectly related in a physical or logisticalway. For example, a target set of data 205 may include a positional datatype such as latitudinal position and longitudinal position, and a speeddata type such as vehicle speed. The positional data may be obtainedfrom a locating device onboard the asset tracking device 200 (e.g., aGPS module), and the vehicle speed data may be obtained through anonboard diagnostic port of the asset 202. Although collected fromdifferent sources, these data types may be related, in that position maybe impacted by vehicle speed, and vice versa. Since these data types areindirectly related in some physical or logistical way, these data typesmay exhibit similar signal patterns. For example, each of these datatypes may go through periods of relative activity and inactivity duringthe same periods of time (e.g., each data type may show high periods ofactivity during periods of acceleration, which results in many datapoints being saved, and periods of low activity during periods of smoothdriving, which results in few data points being saved). It may beparticularly advantageous to bundle together such related data types forstreamlined dataset simplification in a target set of data 205 so thatthe controller 230 performs a dataset simplification algorithm over eachperiod of relative activity and inactivity only once, rather than havingthe controller 230 do the work to rediscover, for each data typeindependently, that each data type goes through substantiallysynchronous periods of relative activity and inactivity. Thus, bundlingtogether indirectly related data types, may achieve furthercomputational efficiencies. In order to perform a multidimensionaldataset simplification algorithm on unrelated (or related) data types,the data may be scaled, as discussed above, and a common threshold valuefor simplification can be used for each data type.

It should be recognized that features and aspects of the variousexamples provided above can be combined into further examples that alsofall within the scope of the present disclosure. The scope of the claimsshould not be limited by the above examples but should be given thebroadest interpretation consistent with the description as a whole.

1. A method comprising: obtaining raw data at a telematics deviceonboard an asset, the asset being a vehicle or associated with avehicle; when a data logging trigger is satisfied, reducing a target setof data within the raw data into a simplified set of data, wherein thereducing comprises: determining a number of dimensions in the target setof data; and in response to determining that the target set of datacontains at least a first dimension for a sequence of time values, asecond dimension for a first type of data collected with respect to thesequence of time values, and a third dimension for a second type of datacollected with respect to the sequence of time values, applying, to thetarget set of data, a dimensionally generalized Ramer-Douglas-Peuckeralgorithm, in which points are selected from the target set of data forinclusion in the simplified set of data based on distances of the pointsto iteratively defined reference lines defined through portions of thetarget set of data in N-dimensional space, N being three or greater; andtransmitting the simplified set of data from the telematics device to aserver for provision of a telematics service.
 2. The method of claim 1,wherein: the distances are minimum distances; and determination of eachminimum distance for a sample point in the target set of data to thereference line comprises: determining a reference point on the referenceline that is minimally distant from that sample point by evaluatingt_(min) in the following equation:$t_{\min} = \frac{t_{p} + {\sum_{n = 1}^{N}{\left( {y_{p_{n}} - y_{0_{n}}} \right)*a_{n}}}}{1 + {\sum_{n = 1}^{N}a_{n}^{2}}}$wherein, t_(p) represents the time value (t) at the sample point (p), Nrepresents the total number of non-time dimensions in the target set ofdata, n represents the n^(th) dimension in the target set of data, y_(p)_(n) represents the value (y) of the sample point (p) in the n^(th)dimension (n), y₀ _(n) represents the value (y) of the first point (0)in the n^(th) dimension (n), the first point (0) being first withrespect to time, a_(n) represents the slope (a) of the reference line inthe n^(th) dimension (n) with respect to time, and t_(min) representsthe time (t) at the reference point, the point at which the referenceline is minimally distant (min) from the sample point; and determiningthe distance from that sample point to the reference point by thefollowing equation:${❘{R\left( {L,p} \right)}❘} = \sqrt{\left( {t_{\min} - t_{p}} \right)^{2} + {\sum\limits_{n = 1}^{N}\left( {y_{0_{n}} + {a_{n}*t_{\min}} - y_{p_{n}}} \right)^{2}}}$wherein, t_(min), t_(p), N, n, y₀ _(n) , a_(n), and y_(p) _(n) aredefined as in the previous equation, and |R(L, p)| represents thedistance (R) from the sample point (p) to the reference point on thereference line (L).
 3. The method of claim 1, wherein: the first type ofdata and the second type of data are recorded in different units, andthe method further comprises scaling the units of one or both of thefirst type of data and the second type of data prior to reduction of thetarget set of data into the simplified set of data.
 4. The method ofclaim 3, wherein the first type of data and the second type of data thatare recorded in different units are obtained from different datasources.
 5. The method of claim 1, wherein the first type of dataincludes a latitudinal position of the asset and the second type of dataincludes a longitudinal position of the asset.
 6. The method of claim 5,wherein: the target set of data further includes a fourth dimension fora third type of data, and the third type of data includes an altitudinalposition of the asset.
 7. The method of claim 1, wherein: the first typeof data includes an accelerometer signal in an X direction, the secondtype of data includes an accelerometer signal in a Y direction, thetarget set of data further includes a fourth dimension for a third typeof data; and the third type of data includes an accelerometer signal ina Z direction.
 8. The method of claim 1, wherein the raw data isobtained from a data source that includes one or more of: an onboarddiagnostic port of the asset and a sensor of an asset tracking device.9. The method of claim 1, wherein each reference line is defined by aninitial point in the portion of the target set of data with a minimumvalue along the first dimension and a final point in the portion of thetarget set of data with a maximum value along the first dimension. 10.The method of claim 1, wherein the asset tracking device is integratedinto the asset.
 11. An asset tracking device for use onboard an asset,the asset tracking device comprising: an interface layer to obtain rawdata from a data source onboard the asset, the asset being a vehicle orassociated with a vehicle; wherein the raw data contains a target set ofdata that contains time-variant N-dimensional data, N being three orgreater; a memory to store the raw data; a controller configured to:determine a number of dimensions in the target set of data, wherein thetarget set of data contains at least a first dimension for a sequence oftime values, a second dimension for a first type of data collected withrespect to the sequence of time values, and a third dimension for asecond type of data collected with respect to the sequence of timevalues; apply a dimensionally generalized Ramer-Douglas-Peuckeralgorithm to the target set of data; reduce the target data set to asimplified set of data by selecting points with the dimensionallygeneralized Ramer-Douglas-Peucker algorithm, wherein points are selectedfrom the target set of data for inclusion in the simplified set of databased on distances of the points to iteratively defined reference linesdefined through portions of the target set of data in N-dimensionalspace, N being three or greater; and a communication interfaceconfigured to transmit the simplified set of data to a server.
 12. Theasset tracking device of claim 11, wherein the distances are minimumdistances and wherein the controller is configured to determine eachminimum distance for a sample point in the target set of data to thereference line by determining a reference point on the reference linethat is minimally distant from that sample point by evaluating t_(min)in the following equation:$t_{\min} = \frac{t_{p} + {\sum_{n = 1}^{N}{\left( {y_{p_{n}} - y_{0_{n}}} \right)*a_{n}}}}{1 + {\sum_{n = 1}^{N}a_{n}^{2}}}$wherein, t_(p) represents the time value (t) at the sample point (p), Nrepresents the total number of non-time dimensions in the target set ofdata, n represents the n^(th) dimension in the target set of data, y_(p)_(n) represents the value (y) of the sample point (p) in the n^(th)dimension (n), y₀ _(n) represents the value (y) of the first point (0)in the n^(th) dimension (n), the first point (0) being first withrespect to time, a_(n) represents the slope (a) of the reference line inthe n^(th) dimension (n) with respect to time, and t_(min) representsthe time (t) at the reference point, the point at which the referenceline is minimally distant (min) from the sample point; and determiningthe distance from that sample point to the reference point by thefollowing equation:${❘{R\left( {L,p} \right)}❘} = \sqrt{\left( {t_{\min} - t_{p}} \right)^{2} + {\sum\limits_{n = 1}^{N}\left( {y_{0_{n}} + {a_{n}*t_{\min}} - y_{p_{n}}} \right)^{2}}}$wherein, t_(min), t_(p), N, n, y₀ _(n) , a_(n), and y_(p) _(n) aredefined as in the previous equation, and |R(L, p)| represents thedistance (R) from the sample point (p) to the reference point on thereference line (L).
 13. The asset tracking device of claim 11, whereinthe first type of data and the second type of data are recorded indifferent units and wherein the controller is configured to scale theunits of one or both of the first type of data and the second type ofdata prior to reduction of the target set of data into the simplifiedset of data.
 14. The asset tracking device of claim 13, wherein thefirst type of data and the second type of data that are recorded indifferent units are obtained from different data sources.
 15. The assettracking device of claim 11, wherein the first type of data includes alatitudinal position of the asset and the second type of data includes alongitudinal position of the asset.
 16. The asset tracking device ofclaim 15, wherein: the target set of data further includes a fourthdimension for a third type of data, and the third type of data includesan altitudinal position of the asset.
 17. The asset tracking device ofclaim 11, wherein: the first type of data includes an accelerometersignal in an X direction, the second type of data includes anaccelerometer signal in a Y direction, the target set of data furtherincludes a fourth dimension for a third type of data; and the third typeof data includes an accelerometer signal in a Z direction.
 18. The assettracking device of claim 11, wherein the raw data is obtained from adata source that includes one or more of: an onboard diagnostic port ofthe asset and a sensor of the asset tracking device.
 19. The assettracking device of claim 11, wherein each reference line is defined byan initial point in the portion of the target set of data with a minimumvalue along the first dimension and a final point in the portion of thetarget set of data with a maximum value along the first dimension. 20.The asset tracking device of claim 11, wherein the asset tracking deviceis integrated into the asset.